Ajim Uddin

Ajim Uddin, Ph.D.

Martin Tuchman School of Management, New Jersey Institute of Technology

About

I am an Assistant Professor of Financial Technology at Martin Tuchman School of Management (MTSM), New Jersey Institute of Technology (NJIT). In the broadest sense, my research interest is Machine Learning and Data Mining with Application to Finance. I am currently working on nonlinear tensor factorization and network representation for financial markets. Primarily my focus is on modeling dynamic changes in network structures and incorporating network information into traditional asset pricing models using spectrum analysis and graph neural networks. On a personal level, I like to play Cricket and Soccer. I truly enjoy hiking and reading books. Two of my recent favorite books are "Becoming" - by Michelle Obama and "Born a Crime" - by Trevor Noah. Speaking of Trevor Noah, sometimes I do stand-up comedy, and trust me, I am really good at this.

Recent News / Updates

Nov 2025: I will attend the ACM CIKM 2025 on November 10-14, 2025. See you in Seoul, South Korea
Nov 2025: I will attend the DECISION SCIENCES INSTITUTE ANNUAL CONFERENCE; on November 22-24, 2025. See you in Orlando, FL
August 2025: Our Paper "Neural Instrumented Factorization: Learning Dynamic Asset Pricing Factors and Loadings through Characteristics Control" - is accepted for CIKM 2025
June 2025: Serving as Track Chair for Computer Information Systems at SWDSI 2026.

Research

My research explores intersections of finance, data science, and AI, emphasizing interpretable and ethical applications in asset pricing and finance. Current projects include graph representations of equity markets, explainable ML for financial decisions, and fairness-aware credit models.

Working Papers

Codes and Data

Are Missing Values Important for Earnings Forecast? A Machine Learning Perspective. [Python Code]
Attention Based Dynamic Graph Learning Framework for Asset Pricing. [Python Code]

Publications

Goswami, B., and Uddin, A. (2025). Significance of predictors: revisiting stock return predictions using explainable AI. Annals of Operations Research. [Link]
Uddin, A., Tao, X., and Yu, D. (2024). The Network Factor of Equity Pricing: A Signed Graph Laplacian Approach. Journal of Financial Econometrics. [Link]
Uddin, A., Tao, X., and Yu, D. (2023). Attention Based Dynamic Graph Neural Network for Asset Pricing. Global Finance Journal. [Link]
Uddin, A., Abdullah, M., Chowdhury, M., and Moudud-Ul-Huq, S. (2023). Forecasting Nonperforming Loans Using Machine Learning. Journal of Forecasting. [Link]
Uddin, A., Tao, X., Chou, C., and Yu, D. (2022). Are Missing Values Important for Earnings Forecast? A Machine Learning Perspective. Quantitative Finance. [Link]
Fang, M., Taylor, S., and Uddin, A. (2022). The Network Structure of Overnight Index Swap Rates. Finance Research Letters. [Link]
Chowdhury, M., Meo, M., Uddin, A., and Haque, M. (2021). Asymmetric Effect of Energy Price on Commodity Price: New evidence from NARDL and Time Frequency Wavelet Approaches. Energy. [Link]
Uddin, A., Yu, D. (2020). Latent factor model for asset pricing. Journal of Behavioral and Experimental Finance. [Link]

Gu, J., Ye, J., Uddin, A., and Wang, W.(2024). DySTAGE: Dynamic Graph Representation Learning for Asset Pricing via Spatio-Temporal Attention and Graph Encodings. ICAIF-2024. [Link]
Jiang, S., Uddin, A., Wei, Z., and Yu, D. (2023). The Network of Mutual Funds: A Dynamic Heterogeneous Graph Neural Network for Estimating Mutual Funds Performance. ICAIF-2023. [Link]
Zhou, D., Uddin, A., Shang, Z., Sylla, C., and Yu, D. (2023). A fast non-linear coupled tensor completion algorithm for financial data integration and imputation. ICAIF-2023. [Link]
Varolgunes, U., Zhou, D., Yu, D., and Uddin, A. (2023). NMTucker: Non-linear Matryoshka Tucker Decomposition for Financial Time Series Imputation. ICAIF-2023. [Link]
Rahman, A., Uddin, A., and Wang, G. (2023). HODL: The Hold of Reddit Over the Stock Market. IEEE ICDEW-2023. [Link]
Uddin, A., Tao, X., Chou, C., and Yu, D. (2022). Machine Learning for Earnings Prediction: A Nonlinear Tensor Approach for Data Integration and Completion. ICAIF-2022. [Link]
Zhou, D., Uddin, A., Tao, X., Shang, Z., and Yu, D. (2022). Temporal Bipartite Graph Neural Networks for Bond Prediction. ICAIF-2022. [Link]
Uddin, A., Tao, X., and Yu, D. (2021). Attention Based Dynamic Graph Learning Framework for Asset Pricing. CIKM-2021. [Link]

Students

Ph.D. Students

Bhaskar Goswami

Ph.D. Student, Business Data Science

Research: ML in Asset Pricing and Data Completion

Ph.D. Committee

Riley Grossman

Ph.D. Student, Business Data Science, NJIT

Research: AI to Detect Deceptive and Illegal Cookie Consent Banner Designs

A M Muntasir Rahman

Ph.D. Computer Science, NJIT

Research: Large Language Models for Financial Sentiment Analysis

Current Position: Post-Doc - Rutgers University, New Brunswick

Undergraduate Researchers

  • Sarisha Sing
  • Megha Saju – Current Position: Finance Co-op HNTB

Teaching

Contact

Email: ajim.uddin@njit.edu

Affiliation: Martin Tuchman School of Management, NJIT, 184-198 Central Ave, Newark, NJ 07103